suppressPackageStartupMessages({
library(tidyverse)
library(lubridate)
library(modelr)
library(broom)
library(lmtest)
library(sandwich)
library(viridis)
})
pm2 <- read_csv("data/pm2.csv", show_col_types = FALSE)
pm2 <- pm2 %>%
mutate(
fnr = str_sub(knr, 1,2),
aar_f = str_sub(aar)
)
head(pm2)
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pm2 %>%
mutate(
fnr = parse_factor(fnr, levels = fnr),
aar_f = parse_factor(aar_f, levels = aar_f)
)
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pm2 <- pm2 %>%
mutate(
Trade_pc_100K = Trade_p/100000
)
head(pm2, n = 4)
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tibble("knr", "fnr", "aar_f", "Trade_pc_100k")
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#Modell
mod1 <- 'pm2 ~ aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
lm1 <- lm(mod1, data = pm2, subset = complete.cases(pm2))
Vi legger inn residualene fra den linære modellen
pm2 %>%
add_residuals(lm1)
head(pm2, n = 4)
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head(pm2, n = 4)
summary(lm1)
ut i fra verdien på års koeffisientene kan vi lese at det er en økning på o,1% fra 2010 til 2017. vi ser en økning fra år til år.
ut i fra vår kunnskap er vi enige om at de er som forventet
Heteroskedastisitet
bptest(lm1)
Ja vi har problemer med heteroskedasiteten her pga at verdien er for høy.
Rapportere robuste standard feil og tilhørende robuste t-verdier
coeftest(lm1)
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t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -20400.742 2663.022 -7.6607 2.790e-14 ***
aar_f2009 104.150 244.767 0.4255 0.6705118
aar_f2010 908.129 245.156 3.7043 0.0002174 ***
aar_f2011 1663.926 245.857 6.7679 1.685e-11 ***
aar_f2012 2240.475 247.095 9.0672 < 2.2e-16 ***
aar_f2013 2869.297 248.315 11.5551 < 2.2e-16 ***
aar_f2014 2863.224 250.537 11.4283 < 2.2e-16 ***
aar_f2015 3525.223 253.083 13.9291 < 2.2e-16 ***
aar_f2016 4274.990 255.812 16.7114 < 2.2e-16 ***
aar_f2017 5146.326 258.498 19.9086 < 2.2e-16 ***
Total_ya_p 582.436 38.941 14.9568 < 2.2e-16 ***
inc_k1 -376.989 30.291 -12.4455 < 2.2e-16 ***
inc_k5 194.354 22.871 8.4979 < 2.2e-16 ***
uni_k_mf -82.023 29.424 -2.7876 0.0053574 **
uni_l_mf 1206.857 42.219 28.5853 < 2.2e-16 ***
Trade_pc_100K 871.993 218.422 3.9922 6.768e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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vcovHC(lm1)
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(Intercept) aar_f2009 aar_f2010 aar_f2011 aar_f2012 aar_f2013
(Intercept) 9297989.37 -26519.17426 -34751.3931 -64358.9799 -88195.7750 -93332.21682
aar_f2009 -26519.17 42579.51052 22306.6988 22379.0191 22461.1963 22562.49160
aar_f2010 -34751.39 22306.69876 41857.2132 22643.0594 22816.5776 23016.04825
aar_f2011 -64358.98 22379.01911 22643.0594 45210.7304 23406.9880 23690.13111
aar_f2012 -88195.78 22461.19628 22816.5776 23406.9880 47055.4187 24270.53282
aar_f2013 -93332.22 22562.49160 23016.0483 23690.1311 24270.5328 49220.90256
aar_f2014 -128032.51 22647.20878 23232.1454 24076.5421 24791.9383 25428.88146
aar_f2015 -177893.27 22637.74268 23267.9132 24237.7165 25055.0255 25755.44730
aar_f2016 -229170.12 22623.80635 23323.0788 24446.1520 25385.7301 26135.55952
aar_f2017 -231919.09 22624.44448 23352.3686 24515.4258 25408.7607 26169.54649
Total_ya_p -134378.95 89.41919 277.8154 681.8928 1112.5721 1311.74280
inc_k1 -48847.48 -46.78668 -117.7882 188.8338 193.4766 -23.25608
inc_k5 -26724.41 110.78484 126.8286 397.1950 455.5137 419.80206
uni_k_mf -23624.40 -129.42390 -212.3787 -468.5265 -572.7298 -695.90501
uni_l_mf 79213.28 -45.36231 -237.3954 -324.3915 -491.9711 -632.27758
Trade_pc_100K 145568.84 497.16540 1261.8579 987.3383 936.1196 2510.69810
aar_f2014 aar_f2015 aar_f2016 aar_f2017 Total_ya_p inc_k1
(Intercept) -128032.5143 -177893.2733 -229170.1243 -231919.0869 -134378.94615 -48847.47803
aar_f2009 22647.2088 22637.7427 22623.8064 22624.4445 89.41919 -46.78668
aar_f2010 23232.1454 23267.9132 23323.0788 23352.3686 277.81538 -117.78822
aar_f2011 24076.5421 24237.7165 24446.1520 24515.4258 681.89276 188.83384
aar_f2012 24791.9383 25055.0255 25385.7301 25408.7607 1112.57212 193.47663
aar_f2013 25428.8815 25755.4473 26135.5595 26169.5465 1311.74280 -23.25608
aar_f2014 53475.4422 27156.8674 27482.0673 27045.3309 1662.72401 237.99318
aar_f2015 27156.8674 63394.1122 28309.5656 27655.2812 2349.75511 438.18220
aar_f2016 27482.0673 28309.5656 75087.4602 28071.1160 3130.99055 706.91052
aar_f2017 27045.3309 27655.2812 28071.1160 89424.5717 3266.65535 723.96826
Total_ya_p 1662.7240 2349.7551 3130.9906 3266.6554 2167.75020 426.37025
inc_k1 237.9932 438.1822 706.9105 723.9683 426.37025 801.89764
inc_k5 750.9501 927.6337 1166.2786 1178.1709 133.21845 496.44438
uni_k_mf -198.2867 136.4018 -110.1222 -816.2879 51.21924 158.26504
uni_l_mf -2195.0185 -3034.7846 -2540.7427 -1110.7783 -614.02732 -500.25996
Trade_pc_100K 2684.4013 2764.2300 282.6406 1862.4720 -1619.34164 -2293.03278
inc_k5 uni_k_mf uni_l_mf Trade_pc_100K
(Intercept) -26724.4053 -23624.40438 79213.27980 145568.8365
aar_f2009 110.7848 -129.42390 -45.36231 497.1654
aar_f2010 126.8286 -212.37867 -237.39541 1261.8579
aar_f2011 397.1950 -468.52650 -324.39148 987.3383
aar_f2012 455.5137 -572.72977 -491.97106 936.1196
aar_f2013 419.8021 -695.90501 -632.27758 2510.6981
aar_f2014 750.9501 -198.28673 -2195.01848 2684.4013
aar_f2015 927.6337 136.40176 -3034.78456 2764.2300
aar_f2016 1166.2786 -110.12216 -2540.74265 282.6406
aar_f2017 1178.1709 -816.28793 -1110.77830 1862.4720
Total_ya_p 133.2185 51.21924 -614.02732 -1619.3416
inc_k1 496.4444 158.26504 -500.25996 -2293.0328
inc_k5 547.3448 104.53767 -690.28424 -115.1786
uni_k_mf 104.5377 1515.96690 -2398.54359 -2608.7728
uni_l_mf -690.2842 -2398.54359 5463.68941 651.9410
Trade_pc_100K -115.1786 -2608.77275 651.94105 60897.1826
=======
(Intercept) aar_f2009 aar_f2010 aar_f2011 aar_f2012 aar_f2013 aar_f2014 aar_f2015
(Intercept) 9297989.37 -26519.17426 -34751.3931 -64358.9799 -88195.7750 -93332.21682 -128032.5143 -177893.2733
aar_f2009 -26519.17 42579.51052 22306.6988 22379.0191 22461.1963 22562.49160 22647.2088 22637.7427
aar_f2010 -34751.39 22306.69876 41857.2132 22643.0594 22816.5776 23016.04825 23232.1454 23267.9132
aar_f2011 -64358.98 22379.01911 22643.0594 45210.7304 23406.9880 23690.13111 24076.5421 24237.7165
aar_f2012 -88195.78 22461.19628 22816.5776 23406.9880 47055.4187 24270.53282 24791.9383 25055.0255
aar_f2013 -93332.22 22562.49160 23016.0483 23690.1311 24270.5328 49220.90256 25428.8815 25755.4473
aar_f2014 -128032.51 22647.20878 23232.1454 24076.5421 24791.9383 25428.88146 53475.4422 27156.8674
aar_f2015 -177893.27 22637.74268 23267.9132 24237.7165 25055.0255 25755.44730 27156.8674 63394.1122
aar_f2016 -229170.12 22623.80635 23323.0788 24446.1520 25385.7301 26135.55952 27482.0673 28309.5656
aar_f2017 -231919.09 22624.44448 23352.3686 24515.4258 25408.7607 26169.54649 27045.3309 27655.2812
Total_ya_p -134378.95 89.41919 277.8154 681.8928 1112.5721 1311.74280 1662.7240 2349.7551
inc_k1 -48847.48 -46.78668 -117.7882 188.8338 193.4766 -23.25608 237.9932 438.1822
inc_k5 -26724.41 110.78484 126.8286 397.1950 455.5137 419.80206 750.9501 927.6337
uni_k_mf -23624.40 -129.42390 -212.3787 -468.5265 -572.7298 -695.90501 -198.2867 136.4018
uni_l_mf 79213.28 -45.36231 -237.3954 -324.3915 -491.9711 -632.27758 -2195.0185 -3034.7846
Trade_pc_100K 145568.84 497.16540 1261.8579 987.3383 936.1196 2510.69810 2684.4013 2764.2300
aar_f2016 aar_f2017 Total_ya_p inc_k1 inc_k5 uni_k_mf uni_l_mf Trade_pc_100K
(Intercept) -229170.1243 -231919.0869 -134378.94615 -48847.47803 -26724.4053 -23624.40438 79213.27980 145568.8365
aar_f2009 22623.8064 22624.4445 89.41919 -46.78668 110.7848 -129.42390 -45.36231 497.1654
aar_f2010 23323.0788 23352.3686 277.81538 -117.78822 126.8286 -212.37867 -237.39541 1261.8579
aar_f2011 24446.1520 24515.4258 681.89276 188.83384 397.1950 -468.52650 -324.39148 987.3383
aar_f2012 25385.7301 25408.7607 1112.57212 193.47663 455.5137 -572.72977 -491.97106 936.1196
aar_f2013 26135.5595 26169.5465 1311.74280 -23.25608 419.8021 -695.90501 -632.27758 2510.6981
aar_f2014 27482.0673 27045.3309 1662.72401 237.99318 750.9501 -198.28673 -2195.01848 2684.4013
aar_f2015 28309.5656 27655.2812 2349.75511 438.18220 927.6337 136.40176 -3034.78456 2764.2300
aar_f2016 75087.4602 28071.1160 3130.99055 706.91052 1166.2786 -110.12216 -2540.74265 282.6406
aar_f2017 28071.1160 89424.5717 3266.65535 723.96826 1178.1709 -816.28793 -1110.77830 1862.4720
Total_ya_p 3130.9906 3266.6554 2167.75020 426.37025 133.2185 51.21924 -614.02732 -1619.3416
inc_k1 706.9105 723.9683 426.37025 801.89764 496.4444 158.26504 -500.25996 -2293.0328
inc_k5 1166.2786 1178.1709 133.21845 496.44438 547.3448 104.53767 -690.28424 -115.1786
uni_k_mf -110.1222 -816.2879 51.21924 158.26504 104.5377 1515.96690 -2398.54359 -2608.7728
uni_l_mf -2540.7427 -1110.7783 -614.02732 -500.25996 -690.2842 -2398.54359 5463.68941 651.9410
Trade_pc_100K 282.6406 1862.4720 -1619.34164 -2293.03278 -115.1786 -2608.77275 651.94105 60897.1826
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Flytter residualene fra lm1 til datasettet pm2
pm2 <- pm2 %>%
add_residuals(lm1)
lager ny variabel
pm2 <- pm2 %>%
mutate(aar_d = make_date(aar))
Vi filterer ut fylkene Østfold, Akershus, Oslo, Rogaland og Hordaland
pm2 <- pm2 %>%
mutate(fylke = substr(knr, start = 1, stop = 2))
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# Her overskriver dere pm2. Lager heller en ny pm2_red
# Bruker pm2_red for å lage plottene
=======
# Her overskriver dere pm2. Lager heller en ny pm2_red
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# Her overskriver dere pm2. Lager heller en ny pm2_red
# Bruker pm2_red for å lage plottene
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pm2_red <- pm2 %>%
filter(fylke %in% c("01", "02", "03", "11", "12"))
Error: Problem with `filter()` input `..1`.
i Input `..1` is `fylke %in% c("01", "02", "03", "11", "12")`.
x object 'fylke' not found
Run `rlang::last_error()` to see where the error occurred.
Oppgave 7 til 10
# pm2_red %>%
# unnest(c(fylke)) %>%
# group_by(fylke, aar_d) %>%
# summarise(mean_fylke = mean(resid)) %>%
# ggplot(mapping = aes(x = aar_d, y = mean_fylke, colour = fylke)) +
# geom_line(lwd = 1) +
# geom_hline(yintercept = 0, colour = "white") +
# theme(legend.position = "bottom")
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pm2_red %>%
filter(fylke %in% c("01", "02", "03", "11", "12")) %>%
unnest(c(fylke)) %>%
group_by (fylke, aar_d) %>%
summarise(mean_fylke = mean(resid)) %>%
ggplot(aes(x=aar_d, y=mean_fylke, colour = fylke)) +
geom_line(lwd=1) +
geom_hline(yintercept = 0, colour = "black") +
theme(legend.position = "bottom")
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`summarise()` has grouped output by 'fylke'. You can override using the `.groups` argument.

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#Dummy fylke og år
Vi innfører en dummy for hvert fylke
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# Nå har vi hel pm2 å bruke
mod2 <- 'pm2 ~ fnr*aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
=======
mod2 <- 'pm2 ~ fnr*aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
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# Nå har vi hel pm2 å bruke
mod2 <- 'pm2 ~ fnr*aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
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Vi genererer lm 2 fra modell 2 og datasettet pm2
lm2 <- lm(mod2, data = pm2)
summary(lm2)
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Call:
lm(formula = mod2, data = pm2)
Residuals:
Min 1Q Median 3Q Max
-8546 -1191 32 1198 8328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21200.688 2521.645 -8.407 < 2e-16 ***
fnr02 -1482.789 702.970 -2.109 0.035045 *
fnr03 3248.234 2190.443 1.483 0.138260
fnr04 -1049.219 774.264 -1.355 0.175537
fnr05 -1937.388 758.293 -2.555 0.010696 *
fnr06 -2172.731 772.094 -2.814 0.004941 **
fnr07 -737.995 1080.348 -0.683 0.494620
fnr08 -3213.279 878.620 -3.657 0.000262 ***
fnr09 -1219.813 913.691 -1.335 0.182020
fnr10 -281.375 852.265 -0.330 0.741323
fnr11 -565.360 771.927 -0.732 0.464012
fnr12 -903.071 742.464 -1.216 0.224012
fnr14 -3339.829 1182.013 -2.826 0.004768 **
fnr15 -3619.198 715.832 -5.056 4.69e-07 ***
fnr16 -1093.217 759.677 -1.439 0.150296
fnr17 -2005.965 917.216 -2.187 0.028860 *
fnr18 -1567.503 774.530 -2.024 0.043126 *
fnr19 -2856.881 1326.142 -2.154 0.031341 *
fnr20 -2656.315 1180.088 -2.251 0.024500 *
aar_f2009 94.009 744.240 0.126 0.899496
aar_f2010 417.129 744.379 0.560 0.575290
aar_f2011 1280.914 744.731 1.720 0.085597 .
aar_f2012 1455.525 745.679 1.952 0.051088 .
aar_f2013 2479.533 746.367 3.322 0.000910 ***
aar_f2014 2795.831 747.254 3.741 0.000188 ***
aar_f2015 3987.973 748.109 5.331 1.09e-07 ***
aar_f2016 5264.965 749.169 7.028 2.89e-12 ***
aar_f2017 6618.572 749.430 8.831 < 2e-16 ***
Total_ya_p 511.787 36.100 14.177 < 2e-16 ***
inc_k1 -243.050 27.007 -9.000 < 2e-16 ***
inc_k5 251.645 22.916 10.981 < 2e-16 ***
uni_k_mf 178.253 28.157 6.331 3.02e-10 ***
uni_l_mf 732.442 42.235 17.342 < 2e-16 ***
Trade_pc_100K 1067.760 190.885 5.594 2.54e-08 ***
fnr02:aar_f2009 -40.505 978.026 -0.041 0.966969
fnr03:aar_f2009 84.133 3068.211 0.027 0.978127
fnr04:aar_f2009 -330.219 1089.318 -0.303 0.761813
fnr05:aar_f2009 416.862 1069.758 0.390 0.696816
fnr06:aar_f2009 -163.759 1089.292 -0.150 0.880516
fnr07:aar_f2009 134.353 1525.051 0.088 0.929808
fnr08:aar_f2009 329.317 1240.237 0.266 0.790631
fnr09:aar_f2009 686.715 1288.922 0.533 0.594245
fnr10:aar_f2009 231.288 1199.909 0.193 0.847172
fnr11:aar_f2009 -414.412 1069.772 -0.387 0.698515
fnr12:aar_f2009 21.853 1036.805 0.021 0.983186
fnr14:aar_f2009 -220.698 1663.985 -0.133 0.894498
fnr15:aar_f2009 205.720 998.429 0.206 0.836779
fnr16:aar_f2009 -346.631 1069.772 -0.324 0.745955
fnr17:aar_f2009 -288.412 1288.940 -0.224 0.822969
fnr18:aar_f2009 -148.285 1089.412 -0.136 0.891744
fnr19:aar_f2009 453.061 1872.733 0.242 0.808864
fnr20:aar_f2009 -927.061 1664.164 -0.557 0.577542
fnr02:aar_f2010 792.694 978.020 0.811 0.417747
fnr03:aar_f2010 2004.378 3068.354 0.653 0.513677
fnr04:aar_f2010 -191.813 1089.355 -0.176 0.860250
fnr05:aar_f2010 655.342 1069.794 0.613 0.540221
fnr06:aar_f2010 189.332 1089.409 0.174 0.862046
fnr07:aar_f2010 728.914 1525.112 0.478 0.632745
fnr08:aar_f2010 1281.636 1240.345 1.033 0.301597
fnr09:aar_f2010 986.486 1288.914 0.765 0.444149
fnr10:aar_f2010 924.121 1199.916 0.770 0.441302
fnr11:aar_f2010 642.468 1069.866 0.601 0.548235
fnr12:aar_f2010 381.898 1036.801 0.368 0.712658
fnr14:aar_f2010 536.844 1663.957 0.323 0.747009
fnr15:aar_f2010 548.008 998.671 0.549 0.583249
fnr16:aar_f2010 -237.962 1069.934 -0.222 0.824020
fnr17:aar_f2010 -422.338 1289.001 -0.328 0.743214
fnr18:aar_f2010 402.939 1089.510 0.370 0.711545
fnr19:aar_f2010 982.125 1872.779 0.524 0.600045
fnr20:aar_f2010 -547.207 1664.063 -0.329 0.742313
fnr02:aar_f2011 992.480 978.070 1.015 0.310359
fnr03:aar_f2011 3891.025 3068.768 1.268 0.204970
fnr04:aar_f2011 -775.700 1089.399 -0.712 0.476523
fnr05:aar_f2011 183.865 1069.834 0.172 0.863563
fnr06:aar_f2011 33.963 1089.394 0.031 0.975132
fnr07:aar_f2011 275.017 1525.266 0.180 0.856930
fnr08:aar_f2011 646.495 1240.336 0.521 0.602269
fnr09:aar_f2011 599.582 1288.944 0.465 0.641860
fnr10:aar_f2011 168.648 1199.944 0.141 0.888243
fnr11:aar_f2011 1243.418 1070.024 1.162 0.245359
fnr12:aar_f2011 165.379 1036.901 0.159 0.873297
fnr14:aar_f2011 1984.847 1664.012 1.193 0.233090
fnr15:aar_f2011 463.880 998.884 0.464 0.642414
fnr16:aar_f2011 -497.945 1069.952 -0.465 0.641705
fnr17:aar_f2011 257.671 1289.086 0.200 0.841590
fnr18:aar_f2011 252.454 1089.674 0.232 0.816812
fnr19:aar_f2011 -669.729 1872.850 -0.358 0.720682
fnr20:aar_f2011 -542.321 1664.293 -0.326 0.744568
fnr02:aar_f2012 1565.161 978.102 1.600 0.109716
fnr03:aar_f2012 5674.403 3069.281 1.849 0.064642 .
fnr04:aar_f2012 -808.528 1089.510 -0.742 0.458115
fnr05:aar_f2012 820.104 1070.017 0.766 0.443507
fnr06:aar_f2012 800.976 1089.455 0.735 0.462302
fnr07:aar_f2012 1047.940 1525.235 0.687 0.492122
fnr08:aar_f2012 1090.416 1240.413 0.879 0.379470
fnr09:aar_f2012 1071.846 1289.011 0.832 0.405779
fnr10:aar_f2012 321.458 1200.216 0.268 0.788856
fnr11:aar_f2012 1467.212 1070.665 1.370 0.170728
fnr12:aar_f2012 669.171 1037.128 0.645 0.518864
fnr14:aar_f2012 1739.551 1664.177 1.045 0.296018
fnr15:aar_f2012 463.860 999.265 0.464 0.642556
fnr16:aar_f2012 380.682 1070.437 0.356 0.722154
fnr17:aar_f2012 637.493 1289.624 0.494 0.621133
fnr18:aar_f2012 482.679 1089.761 0.443 0.657871
fnr19:aar_f2012 727.671 1872.902 0.389 0.697670
fnr20:aar_f2012 -378.342 1664.741 -0.227 0.820240
fnr02:aar_f2013 1953.373 978.298 1.997 0.045996 *
fnr03:aar_f2013 5108.375 3070.149 1.664 0.096297 .
fnr04:aar_f2013 -1206.685 1089.615 -1.107 0.268240
fnr05:aar_f2013 -198.536 1070.094 -0.186 0.852832
fnr06:aar_f2013 410.281 1089.375 0.377 0.706497
fnr07:aar_f2013 890.998 1525.236 0.584 0.559173
fnr08:aar_f2013 575.599 1240.249 0.464 0.642628
fnr09:aar_f2013 64.585 1289.204 0.050 0.960050
fnr10:aar_f2013 -515.180 1200.200 -0.429 0.667793
fnr11:aar_f2013 1179.371 1071.062 1.101 0.270979
fnr12:aar_f2013 -69.430 1037.183 -0.067 0.946636
fnr14:aar_f2013 208.353 1664.208 0.125 0.900381
fnr15:aar_f2013 7.994 999.213 0.008 0.993617
fnr16:aar_f2013 -347.235 1070.757 -0.324 0.745754
fnr17:aar_f2013 203.405 1289.762 0.158 0.874704
fnr18:aar_f2013 201.272 1090.026 0.185 0.853524
fnr19:aar_f2013 278.261 1873.128 0.149 0.881921
fnr20:aar_f2013 -1110.163 1664.836 -0.667 0.504960
fnr02:aar_f2014 2019.269 978.649 2.063 0.039214 *
fnr03:aar_f2014 4938.603 3071.105 1.608 0.107979
fnr04:aar_f2014 -1456.367 1089.708 -1.336 0.181550
fnr05:aar_f2014 -254.055 1070.253 -0.237 0.812388
fnr06:aar_f2014 571.152 1089.474 0.524 0.600167
fnr07:aar_f2014 582.123 1525.332 0.382 0.702772
fnr08:aar_f2014 689.084 1240.251 0.556 0.578548
fnr09:aar_f2014 -186.541 1289.179 -0.145 0.884965
fnr10:aar_f2014 -674.319 1200.339 -0.562 0.574335
fnr11:aar_f2014 -183.391 1071.523 -0.171 0.864124
fnr12:aar_f2014 -147.825 1037.277 -0.143 0.886690
fnr14:aar_f2014 253.302 1664.812 0.152 0.879084
fnr15:aar_f2014 -481.056 999.093 -0.481 0.630220
fnr16:aar_f2014 -229.362 1070.812 -0.214 0.830418
fnr17:aar_f2014 -61.073 1289.824 -0.047 0.962239
fnr18:aar_f2014 -393.115 1090.258 -0.361 0.718459
fnr19:aar_f2014 1688.165 1873.121 0.901 0.367563
fnr20:aar_f2014 -1563.827 1665.176 -0.939 0.347778
fnr02:aar_f2015 2401.120 979.036 2.453 0.014273 *
fnr03:aar_f2015 6985.367 3073.112 2.273 0.023131 *
fnr04:aar_f2015 -1912.336 1089.754 -1.755 0.079446 .
fnr05:aar_f2015 -1326.089 1070.254 -1.239 0.215480
fnr06:aar_f2015 22.631 1089.626 0.021 0.983431
fnr07:aar_f2015 990.944 1525.354 0.650 0.515996
fnr08:aar_f2015 -776.910 1240.290 -0.626 0.531130
fnr09:aar_f2015 -1242.730 1289.232 -0.964 0.335201
fnr10:aar_f2015 -1492.749 1200.502 -1.243 0.213856
fnr11:aar_f2015 -1489.385 1072.451 -1.389 0.165063
fnr12:aar_f2015 -711.755 1037.476 -0.686 0.492767
fnr14:aar_f2015 -1695.187 1665.139 -1.018 0.308783
fnr15:aar_f2015 -587.449 999.385 -0.588 0.556727
fnr16:aar_f2015 -139.973 1070.880 -0.131 0.896019
fnr17:aar_f2015 -867.834 1289.740 -0.673 0.501107
fnr18:aar_f2015 -439.127 1090.372 -0.403 0.687190
fnr19:aar_f2015 369.085 1873.412 0.197 0.843839
fnr20:aar_f2015 -3266.760 1665.444 -1.961 0.049964 *
fnr02:aar_f2016 3656.344 979.067 3.735 0.000193 ***
fnr03:aar_f2016 10264.572 3074.072 3.339 0.000856 ***
fnr04:aar_f2016 -2459.017 1089.893 -2.256 0.024169 *
fnr05:aar_f2016 -2117.228 1070.338 -1.978 0.048059 *
fnr06:aar_f2016 -598.671 1089.701 -0.549 0.582801
fnr07:aar_f2016 447.813 1525.278 0.294 0.769099
fnr08:aar_f2016 -1716.491 1240.468 -1.384 0.166595
fnr09:aar_f2016 -1987.219 1289.181 -1.541 0.123368
fnr10:aar_f2016 -3090.918 1200.777 -2.574 0.010124 *
fnr11:aar_f2016 -3274.743 1072.946 -3.052 0.002303 **
fnr12:aar_f2016 -901.775 1037.688 -0.869 0.384941
fnr14:aar_f2016 -1552.417 1665.259 -0.932 0.351330
fnr15:aar_f2016 -1872.887 999.582 -1.874 0.061126 .
fnr16:aar_f2016 -1074.143 1070.970 -1.003 0.316004
fnr17:aar_f2016 -1612.215 1290.487 -1.249 0.211703
fnr18:aar_f2016 -1361.291 1090.771 -1.248 0.212178
fnr19:aar_f2016 906.286 1873.612 0.484 0.628646
fnr20:aar_f2016 -3169.910 1665.821 -1.903 0.057200 .
fnr02:aar_f2017 4707.776 979.374 4.807 1.65e-06 ***
fnr03:aar_f2017 13986.613 3075.071 4.548 5.74e-06 ***
fnr04:aar_f2017 -3549.658 1089.920 -3.257 0.001146 **
fnr05:aar_f2017 -2397.820 1070.176 -2.241 0.025165 *
fnr06:aar_f2017 60.036 1089.704 0.055 0.956069
fnr07:aar_f2017 960.018 1525.236 0.629 0.529146
fnr08:aar_f2017 -2045.538 1240.415 -1.649 0.099294 .
fnr09:aar_f2017 -3223.036 1289.344 -2.500 0.012510 *
fnr10:aar_f2017 -3807.142 1200.767 -3.171 0.001545 **
fnr11:aar_f2017 -3863.610 1073.185 -3.600 0.000326 ***
fnr12:aar_f2017 -2046.447 1038.104 -1.971 0.048828 *
fnr14:aar_f2017 -2074.192 1665.271 -1.246 0.213077
fnr15:aar_f2017 -2799.827 999.681 -2.801 0.005149 **
fnr16:aar_f2017 -2278.453 1070.923 -2.128 0.033499 *
fnr17:aar_f2017 -2761.733 1290.527 -2.140 0.032479 *
fnr18:aar_f2017 -2661.041 1090.689 -2.440 0.014785 *
fnr19:aar_f2017 -716.410 1873.886 -0.382 0.702272
fnr20:aar_f2017 -3922.387 1665.464 -2.355 0.018615 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2105 on 1944 degrees of freedom
Multiple R-squared: 0.8953, Adjusted R-squared: 0.8848
F-statistic: 85.21 on 195 and 1944 DF, p-value: < 2.2e-16
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
Vi legger inn residualene fra lm2 til pm2 og kaller dem res_m2
pm2 <- pm2 %>%
mutate(res_m2 = resid(lm2))
Vi filtrer fylkene
pm2 %>% filter(fnr %in% c("01", "02", "04", "11", "12")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
geom_line(aes(group = knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom') +
facet_wrap(~fylke)
<<<<<<< HEAD
=======

>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
kvaliteten på modell 2 ?
er det grunn til å mistenke at vi mangler variabler. ja vi mistenker dette.
Vi filtrerer med hensyn på fylke “11”
pm2 %>% filter(fnr %in% c("11")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "D") +
geom_line(aes(group = knavn, colour = knavn, size = knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
<<<<<<< HEAD
=======

>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
Vi gjentar plottet ovenfor med utvalgte kommuner
pm2 %>% filter(knr %in% c("1119", "1120", "1127", "1121", "1130", "1135", "1106", "1149")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "A") +
geom_line(aes(group = knavn, colour = knavn, size =knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
<<<<<<< HEAD
=======

>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
Det som kjennetegner kommunene i Rogland er at i Stavanger blir overvurdert
#Modell for hvert år
<<<<<<< HEAD
<<<<<<< HEAD
pm2_n <- pm2 %>%
# velger først variablene. Tar med aar_d
select(pm2, fnr, knr, aar, aar_f, aar_d, Menn_ya_p,
Kvinner_ya_p, Total_ya_p, inc_k1, inc_k5, uni_k_mf,
uni_l_mf, Trade_pc_100K) %>%
# grupperer mht. aar_d som er date object
group_by(aar_d) %>%
nest()
=======
=======
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
pm2_n <- pm2 %>%
# velger først variablene. Tar med aar_d
select(pm2, fnr, knr, aar, aar_f, aar_d, Menn_ya_p,
Kvinner_ya_p, Total_ya_p, inc_k1, inc_k5, uni_k_mf,
uni_l_mf, Trade_pc_100K) %>%
# grupperer mht. aar_d som er date object
<<<<<<< HEAD
group_by(aar_d) %>%
nest()
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
=======
group_by(aar_d) %>%
nest()
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
<<<<<<< HEAD
<<<<<<< HEAD
pm2_n
=======
=======
pm2_n
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
# Ser bra ut ;-)
pm2_n$data[[1]] %>%
head(n = 5)
<<<<<<< HEAD
=======
=======
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
dim(pm2_n)
<<<<<<< HEAD
=======
[1] 10 2
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
kom modell
# må bruke a_df i funksjonen ellers vil den alltid bruke pm2
kom_model <- function(a_df) {
lm(pm2 ~ fnr + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K, data = a_df)
}
pm2_n <- pm2_n %>%
mutate(model = map(data, .f = kom_model))
# kom_model(pm2_n$aar) %>%
# summary()
mod_sum <- pm2_n %>%
# Har bare disse årene så unødvendig
# filter(aar %in% c("2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017")) %>%
mutate(mod_summary = map(.x = model, .f = glance)) %>%
unnest(mod_summary) %>%
print()
<<<<<<< HEAD
<<<<<<< HEAD
=======
=======
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64
coef_df <- mod_sum$model %>%
# 1 plukker ut koeffisientene
map_df(1) %>%
# legges i en tibble
tibble()
<<<<<<< HEAD
---
title: "Modeller"
Autor: Ann Elisabth and Heidi 
output: html_notebook
---

```{r}
suppressPackageStartupMessages({
  library(tidyverse)
  library(lubridate)
  library(modelr)
  library(broom)
  library(lmtest)
  library(sandwich)
  library(viridis)
})
```

```{r Les inn fil}
pm2 <- read_csv("data/pm2.csv", show_col_types = FALSE)
```

```{r}
pm2 <- pm2 %>% 
  mutate(
    fnr = str_sub(knr, 1,2),
    aar_f = str_sub(aar)
  )
```

```{r Oversikten over variablene i pm2}
head(pm2)
```

```{r Parse factor}
pm2 %>% 
  mutate(
    fnr = parse_factor(fnr, levels = fnr),
    aar_f = parse_factor(aar_f, levels = aar_f)
  )
```

```{r}
pm2 <- pm2 %>% 
  mutate(
    Trade_pc_100K = Trade_p/100000
  ) 
```

```{r Oversikt}
head(pm2, n = 4)
```

```{r Tabell 1}
tibble("knr", "fnr", "aar_f", "Trade_pc_100k")
```

#Modell

```{r Modell}
mod1 <- 'pm2 ~ aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
```

```{r Genererer lm }
lm1 <- lm(mod1, data = pm2, subset = complete.cases(pm2))
```

Vi legger inn residualene fra den linære modellen

```{r Residualer}
pm2 %>% 
add_residuals(lm1)
head(pm2, n = 4)
```

```{r Oppsummering}
summary(lm1)
```
ut i fra verdien på års koeffisientene kan vi lese at det er en økning på o,1% fra 2010 til 2017. vi ser en økning fra år til år. 

ut i fra vår kunnskap er vi enige om at de er som forventet 

Heteroskedastisitet
```{r}
bptest(lm1)
```

Ja vi har problemer med heteroskedasiteten her pga at verdien er for
høy.

Rapportere robuste standard feil og tilhørende robuste t-verdier

```{r}
coeftest(lm1)
```

```{r}
vcovHC(lm1)
```

Flytter residualene fra lm1 til datasettet pm2

```{r}
pm2 <- pm2 %>%
  add_residuals(lm1)
```


lager ny variabel

```{r}
pm2 <- pm2 %>%
mutate(aar_d = make_date(aar))
```

Vi filterer ut fylkene Østfold, Akershus, Oslo, Rogaland og Hordaland

```{r}
pm2 <- pm2 %>%
mutate(fylke = substr(knr, start = 1, stop = 2))
```

```{r}
# Her overskriver dere pm2. Lager heller en ny pm2_red
# Bruker pm2_red for å lage plottene
pm2_red <- pm2 %>% 
  filter(fylke %in% c("01", "02", "03", "11", "12"))
```

Oppgave 7 til 10

```{r}
# pm2_red %>% 
#   unnest(c(fylke)) %>% 
#   group_by(fylke, aar_d) %>% 
#   summarise(mean_fylke = mean(resid)) %>% 
#   ggplot(mapping = aes(x = aar_d, y = mean_fylke, colour = fylke)) +
#   geom_line(lwd = 1) +
#   geom_hline(yintercept = 0, colour = "white") +
#   theme(legend.position = "bottom")
```

```{r}
pm2_red %>%
  filter(fylke %in% c("01", "02", "03", "11", "12")) %>%
  unnest(c(fylke)) %>%
  group_by (fylke, aar_d) %>%
  summarise(mean_fylke = mean(resid)) %>%
  ggplot(aes(x=aar_d, y=mean_fylke, colour = fylke)) +
  geom_line(lwd=1) +
  geom_hline(yintercept = 0, colour = "black") +
  theme(legend.position = "bottom")
```


#Dummy fylke og år

Vi innfører en dummy for hvert fylke

```{r}
# Nå har vi hel pm2 å bruke
mod2 <- 'pm2 ~ fnr*aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
```

Vi genererer lm 2 fra modell 2 og datasettet pm2

```{r}
lm2 <- lm(mod2, data = pm2)
```

```{r}
summary(lm2)
```

Vi legger inn residualene fra lm2 til pm2 og kaller dem res_m2

```{r}
pm2 <- pm2 %>%
  mutate(res_m2 = resid(lm2))
```

Vi filtrer fylkene

```{r}
pm2 %>% filter(fnr %in% c("01", "02", "04", "11", "12")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
geom_line(aes(group = knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom') +
  facet_wrap(~fylke)
```

i.  kvaliteten på modell 2 ?

ii. er det grunn til å mistenke at vi mangler variabler. ja vi mistenker dette.

Vi filtrerer med hensyn på fylke "11"

```{r}
pm2 %>% filter(fnr %in% c("11")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "D") +
geom_line(aes(group = knavn, colour = knavn, size = knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
```

Vi gjentar plottet ovenfor med utvalgte kommuner

```{r}
pm2 %>% filter(knr %in% c("1119", "1120", "1127", "1121", "1130", "1135", "1106", "1149")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "A") +
geom_line(aes(group = knavn, colour = knavn, size =knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
```

Det som kjennetegner kommunene i Rogland er at i Stavanger blir overvurdert

#Modell for hvert år

i.  

```{r}
pm2_n <- pm2 %>% 
# velger først variablene. Tar med aar_d
   select(pm2, fnr, knr, aar, aar_f, aar_d, Menn_ya_p, 
          Kvinner_ya_p, Total_ya_p, inc_k1, inc_k5, uni_k_mf, 
          uni_l_mf, Trade_pc_100K) %>% 
  # grupperer mht. aar_d som er date object
  group_by(aar_d) %>%
  nest()
```

```{r}
pm2_n
```

```{r}
# Ser bra ut ;-)
pm2_n$data[[1]] %>%
 head(n = 5)
```

```{r}
dim(pm2_n)
```
kom modell
```{r}
# må bruke a_df i funksjonen ellers vil den alltid bruke pm2
kom_model <- function(a_df) {
   lm(pm2 ~ fnr + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K, data = a_df)
}
```

```{r}
pm2_n <- pm2_n %>% 
   mutate(model = map(data, .f = kom_model)) 
```

```{r}
# kom_model(pm2_n$aar) %>% 
#    summary()
```

5. 
```{r}

```

```{r}
mod_sum <- pm2_n %>% 
  # Har bare disse årene så unødvendig
   # filter(aar %in% c("2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017")) %>% 
   mutate(mod_summary = map(.x = model, .f = glance)) %>% 
   unnest(mod_summary) %>% 
   print()
```

```{r}
coef_df <- mod_sum$model %>% 
  # 1 plukker ut koeffisientene
   map_df(1) %>% 
  # legges i en tibble 
   tibble()
```

=======
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
>>>>>>> 13a7866c22d770944d875877fc063cabaa2f5186
=======
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>>>>>>> 9d191ee1d48e508193ed3703a38d953fa7f54d64